QuaDMutEx: quadratic driver mutation explorer

Background Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license

BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

Simultaneous evolutionary expansion and constraint of genomic heterogeneity in multifocal lung cancer.

Recent genomic analyses have revealed substantial tumor heterogeneity across various cancers. However, it remains unclear whether and how genomic heterogeneity is constrained during tumor evolution. Here, we sequence a unique cohort of multiple synchronous lung cancers (MSLCs) to determine the relative diversity and uniformity of genetic drivers upon identical germline and environmental background. We find that each multicentric primary tumor harbors distinct oncogenic alterations, including novel mutations that are experimentally demonstrated to be functional and therapeutically targetable. However, functional studies show a strikingly constrained tumorigenic pathway underlying heterogeneous genetic variants. These results suggest that although the mutation-specific routes that cells take during oncogenesis are stochastic, genetic trajectories may be constrained by selection for functional convergence on key signaling pathways. Our findings highlight the robust evolutionary pressures that simultaneously shape the expansion and constraint of genomic diversity, a principle that holds important implications for understanding tumor evolution and optimizing therapeutic strategies.Across cancer types tumor heterogeneity has been observed, but how this relates to tumor evolution is unclear. Here, the authors sequence multiple synchronous lung cancers, highlighting the evolutionary pressures that simultaneously shape the expansion and constraint of genomic heterogeneity

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information

IAMBEE : a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement

SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background: With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively. Results: SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant. Conclusions: SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes

Efficient algorithms to discover alterations with complementary functional association in cancer

Recent large cancer studies have measured somatic alterations in an unprecedented number of tumours. These large datasets allow the identification of cancer-related sets of genetic alterations by identifying relevant combinatorial patterns. Among such patterns, mutual exclusivity has been employed by several recent methods that have shown its effectivenes in characterizing gene sets associated to cancer. Mutual exclusivity arises because of the complementarity, at the functional level, of alterations in genes which are part of a group (e.g., a pathway) performing a given function. The availability of quantitative target profiles, from genetic perturbations or from clinical phenotypes, provides additional information that can be leveraged to improve the identification of cancer related gene sets by discovering groups with complementary functional associations with such targets. In this work we study the problem of finding groups of mutually exclusive alterations associated with a quantitative (functional) target. We propose a combinatorial formulation for the problem, and prove that the associated computation problem is computationally hard. We design two algorithms to solve the problem and implement them in our tool UNCOVER. We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In addition, our algorithms are much faster than the state-of-the-art, allowing the analysis of large datasets of thousands of target profiles from cancer cell lines. We show that on one such dataset from project Achilles our methods identify several significant gene sets with complementary functional associations with targets.Comment: Accepted at RECOMB 201